在下水道系统中模拟短暂的混合流动,并通过基于物理的机器学习数据融合
Shixun Li1, Wenchong Tian2, Hexiang Yan1
1College of Environmental Science and Engineering, Tongji University, Shanghai 200092, China.
Water research X
|November 5, 2024
概括
一个新的数据驱动模型,即暂时混合流物理信息神经网络 (TMF-PINN),准确地模拟城市排水系统中的复杂流. 这种方法克服了传统方法预测管道爆破和热泉的局限性.
科学领域:
- 环境工程 环境工程
- 计算流体动力学的流体动力学.
- 机器学习 机器学习
背景情况:
- 城市排水系统 (UDS) 面临过渡混合流动的挑战,导致管道爆裂和热泉等问题.
- 传统的机械模型与多源数据集成,复杂方程和高计算成本作斗争.
研究的目的:
- 开发一个数据驱动的模型,TMF-PINN,用于模拟和逆转污水网络中的短暂混合流 (TMF).
- 解决UDS中传统建模方法的局限性.
主要方法:
- 利用物理信息神经网络 (PINN) 整合实验数据,模拟结果和部分微分方程 (PDEs).
- 引入了一个状态因子 (α) 来链接开放通道和加压流动力学.
- 采用富里埃特征提取和二次神经网络,用于高频动态过程捕获.
主要成果:
- TMF-PINN模型准确地预测了UDS中的流场.
- 通过对SWMM和HLL解决方案的三种经典案例进行验证,证明了有效性.
- 绕过了传统方法固有的时空分辨率约束.
结论:
- TMF-PINN模型为模拟城市排水中的短暂混合流提供了强大而准确的解决方案.
- 这种数据驱动的方法增强了UDS中关键事件的预测能力.
- 利用智慧城市水系统数据提高性能.
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